989 citations · 1.3k across the 20 of their papers we have counts for
4 papers · 1 filter
Amazon SageMaker Automatic Model Tuning: Scalable Gradient-Free Optimization
Valerio Perrone, Huibin Shen, Aida Zolic +12
Tuning complex machine learning systems is challenging. Machine learning typically requires to set hyperparameters, be it regularization, architecture, or optimization parameters,…
Cost-aware Bayesian Optimization
Eric Hans Lee, Valerio Perrone, Cedric Archambeau +1
Bayesian optimization (BO) is a class of global optimization algorithms, suitable for minimizing an expensive objective function in as few function evaluations as possible. While B…
Model-based Asynchronous Hyperparameter and Neural Architecture Search
Aaron Klein, Louis C. Tiao, Thibaut Lienart +2
We introduce a model-based asynchronous multi-fidelity method for hyperparameter and neural architecture search that combines the strengths of asynchronous Hyperband and Gaussian p…
LEEP: A New Measure to Evaluate Transferability of Learned Representations
Cuong V. Nguyen, Tal Hassner, Matthias Seeger +1
We introduce a new measure to evaluate the transferability of representations learned by classifiers. Our measure, the Log Expected Empirical Prediction (LEEP), is simple and easy…